Intrinsic excitation-dependent room-temperature internal quantum efficiency of AlGaN nanowires with varying Al contents
Bibliographic record
Abstract
Aluminum gallium nitride (AlGaN) nanowires have become an emerging approach for semiconductor deep ultraviolet light-emitting devices. To further improve the device performance, it is critical to understand the optical quality of AlGaN nanowires. However, today, the room-temperature internal quantum efficiency (IQE) of AlGaN nanowires is predominantly analyzed by the temperature-dependent photoluminescence (PL) approach under one excitation power or taking the PL intensity ratio at the room temperature and low temperature with different excitation powers. In both cases, one needs to assume the low temperature IQE to be 100%, which is not always valid, in particular when the excitation power changes at the low temperature. In this work, we study the room-temperature IQE of AlGaN nanowires through the detailed excitation power-dependent PL experiments and theoretical analysis. This allows us to derive the intrinsic room-temperature IQE of AlGaN nanowires as a function of the excitation power. It is found that for an Al content in the range of 22%–54%, the IQE of all samples increases as the excitation increases, followed by an efficiency droop. Moreover, comparing different samples, the IQE at low excitations increases as the Al content increases, whereas the peak IQE reduces from 73% to 56% as the Al content increases. The underlying mechanisms are also discussed in this paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".